Recommendation systems are commonly used in many computing sectors, particularly those involving internet-based commerce. Many of these systems are designed to recommend items such as movies, music, websites, or other products or services to interested potential customers. Generally, these recommendations are based on information that is available about the individual and the items that might be recommended. In some systems, the recommendations may also be based on the collective taste and preferences of groups of existing or potential customers.
Algorithms used for recommendation systems have traditionally been classified into two types: content-based algorithms and collaborative filtering algorithms. Content-based recommendation algorithms generally analyze the content of items in which a user has shown interest and recommend other items to the user that have similar or comparable content. Collaborative filtering algorithms, on the other hand, typically make automatic predictions about the interests of a user by collecting information from many users and recommend items to individual users based on commonalities in the user's interests and the interests of other users.
Throughout the drawings, identical reference numbers designate similar, but not necessarily identical, elements.